Prediction of jet penetration depth based on least square support vector machine

被引：19

作者：

Wang, Chun-hua ^{[1
]}

Zhong, Zhao-ping ^{[1
]}

Li, Rui ^{[1
]}

E, Jia-qiang ^{[2
]}

机构：

[1] Southeast Univ, Sch Energy & Environm, Nanjing 211189, Peoples R China

[2] Hunan Univ, Sch Mech & Automot Engn, Changsha 410082, Hunan, Peoples R China

来源：

POWDER TECHNOLOGY | 2010年 / 203卷 / 02期

关键词：

Spout-fluidized bed; Jet penetration depth; Least square support vector machine; Chaos optimization algorithm; SPOUT-FLUID BED; GAS; CLASSIFICATION; ALGORITHMS; BEHAVIOR; HEIGHT;

D O I：

10.1016/j.powtec.2010.04.023

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Experiments to investigate the jet penetration depth were carried out. The jet penetration depth increases with the increase of spouting gas velocity, spouting nozzle diameter and carrier gas density, but decreases with the rise of the static bed height, particle density, particle diameter and fluidized gas rate. The intelligent model to predict the jet penetration depth has been established based on least square support vector machine and adaptive mutative scale chaos optimization algorithm. The prediction performance of the intelligent model is better than empirical correlations and neural network. (C) 2010 Elsevier B.V. All rights reserved.

引用

页码：404 / 411

页数：8

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